EasyVideoR1: Easier RL for Video Understanding

2026-04-18

AI Summary

Existing reinforcement learning frameworks for video understanding suffer from high computational overhead in decoding high-dimensional visual inputs, sensitivity to hyperparameters, poor reproducibility, and a lack of modality-specific design. This work proposes the first efficient reinforcement learning training framework tailored for video understanding, which reduces redundant computation through offline preprocessing and tensor caching, introduces a task-aware modular reward mechanism, and employs hybrid training that combines offline high-quality trajectories with online exploration data. The framework further supports joint image–video training and asynchronous multi-benchmark evaluation. Evaluated across 22 mainstream video understanding benchmarks, it achieves reproduction accuracy highly consistent with official reports, delivers a 1.47× improvement in training throughput, and provides unified support for 11 distinct video and image tasks.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve into natively multimodal architectures, extending RLVR to video understanding becomes increasingly important yet remains largely unexplored, due to the diversity of video task types, the computational overhead of repeatedly decoding and preprocessing high-dimensional visual inputs, and the difficulty of reproducible evaluation across numerous sensitive hyperparameters. Existing open-source RL training frameworks provide solid infrastructure for text and image scenarios but lack systematic optimizations tailored for video modality. In this work, we present \textbf{EasyVideoR1}, a complete and efficient reinforcement learning framework specifically designed for training large vision-language models on video understanding tasks. EasyVideoR1 makes the following contributions: (1) a full video RL training pipeline with offline preprocessing and tensor caching that eliminates redundant video decoding and yields a 1.47 $\times$ throughput improvement; (2) a comprehensive, task-aware reward system covering 11 distinct video and image problem types with unified routing and modular extension; (3) a mixed offline-online data training paradigm that combines curated high-quality trajectories with on-policy exploration, benefiting the learning of more challenging tasks; (4) joint image-video training with independently configurable pixel budgets, allowing the two modalities to mutually reinforce each other; and (5) an asynchronous multi-benchmark evaluation framework covering 22 mainstream video understanding benchmarks, with reproduced accuracy closely aligned with officially reported scores.

Problem

• video understanding
• reinforcement learning
• multimodal learning
• computational overhead
• reproducible evaluation

Innovation

• Reinforcement Learning
• Video Understanding
• Multimodal Learning
• Tensor Caching
• Reward Design